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Comparison of Smart Display Versus Laptop Platforms for an eHealth Intervention to Improve Functional Health for Older Adults With Multiple Chronic Conditions: Protocol for a Randomized Clinical Trial

Comparison of Smart Display Versus Laptop Platforms for an eHealth Intervention to Improve Functional Health for Older Adults With Multiple Chronic Conditions: Protocol for a Randomized Clinical Trial

Results indicated that 282 participants after attrition would be needed for 80% power (P With regard to power for moderation analyses, we ran a sensitivity analysis to compute the smallest effect size our study would be powered to find using 280 given 4 groups (eg, 2 study arms × 2 genders). With N=280, G*Power calculations [63] show our study would have a 95% chance (with P Patients will complete surveys at baseline, 6, 12, and 18 months.

David H Gustafson Sr, Marie-Louise Mares, Darcie C Johnston, John J Curtin, Klaren Pe-Romashko, Gina Landucci

JMIR Res Protoc 2025;14:e64449

Innovating Care for Postmenopausal Women Using a Digital Approach for Pelvic Floor Dysfunctions: Prospective Longitudinal Cohort Study

Innovating Care for Postmenopausal Women Using a Digital Approach for Pelvic Floor Dysfunctions: Prospective Longitudinal Cohort Study

Statistical significance was defined as P Of the 3684 participants screened for eligibility, 633 participants were excluded (346 declined participation and 287 did not meet eligibility criteria; Figure 2). The program started with 3051 participants, of which 2367 participants completed it, translating into a completion rate of 77.6%. Study flowchart.

Ana P Pereira, Dora Janela, Anabela C Areias, Maria Molinos, Xin Tong, Virgílio Bento, Vijay Yanamadala, Jennesa Atherton, Fernando Dias Correia, Fabíola Costa

JMIR Mhealth Uhealth 2025;13:e68242

Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis

Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis

A Kruskal-Wallis test demonstrated statistically significant differences across the 5 visit types (P For each included PROM, the text span match frequency and note categorization frequency between human annotation and the rule-based model output on the full training set and in the full-text corpus are presented in Multimedia Appendix 1.

Brian C Coleman, Kelsey L Corcoran, Cynthia A Brandt, Joseph L Goulet, Stephen L Luther, Anthony J Lisi

JMIR Med Inform 2025;13:e66466

Exploring Technical Features to Enhance Control in Videoconferencing Psychotherapy: Quantitative Study on Clinicians’ Perspectives

Exploring Technical Features to Enhance Control in Videoconferencing Psychotherapy: Quantitative Study on Clinicians’ Perspectives

The KMO score was 0.97 and the Bartlett test of sphericity was significant (P Based on the eigenvalue (Table 1), 3 factors needed to be considered, while the Scree plot (Figure 2) showed 4 factors. However, attention was focused on reporting the 3-factor loading (Table 2) since the results obtained for the fourth factor were too weak. The fourth factor was formed by only two items that were not significant, leading to a focus on the 3-factor loading.

Francesco Cataldo, Shanton Chang, Antonette Mendoza, George Buchanan, Nicholas Van Dam

J Med Internet Res 2025;27:e66904

Cooperative Virtual Reality Gaming for Anxiety and Pain Reduction in Pediatric Patients and Their Caregivers During Painful Medical Procedures: Protocol for a Randomized Controlled Trial

Cooperative Virtual Reality Gaming for Anxiety and Pain Reduction in Pediatric Patients and Their Caregivers During Painful Medical Procedures: Protocol for a Randomized Controlled Trial

STAIC-Ta STAIC-Sb VASc anxiety PANAS-Kd SSQ-Ce STAIC-S VAS anxiety PANAS-K VAS pain SSQ-C Adapted PXIf for childreng Patient experience Presence PANAS PANAS-C-Pg VAS anxiety (child) PANAS 0-P VAS anxiety (child) VAS pain (child) PXIh Player experienceg CHEOPSi Self-designed questions for assessing procedure flow a STAIC-T: State-Trait Anxiety Inventory for children–trait anxiety. b STAIC-S: State-Trait Anxiety Inventory for children–state anxiety. c VAS: visual analog scale. d PANAS-K: German translation of the

Stefan Liszio, Franziska Bäuerlein, Jens Hildebrand, Carolin van Nahl, Maic Masuch, Oliver Basu

JMIR Res Protoc 2025;14:e63098

Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study

Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study

The 2-sample t test P values show the 2-hour intervals when TAC was significantly different between the 2 groups. As an additional reference, the logistic regression P values are shown with the TAC 2-hour intervals used as predictors, after adjusting for age, sex, and BMI. MS: multiple sclerosis; PMS: progressive multiple sclerosis; RRMS: relapsing-remitting multiple sclerosis.

Nicole Bou Rjeily, Muraleetharan Sanjayan, Pratim Guha Niyogi, Blake E Dewey, Alexandra Zambriczki Lee, Christy Hulett, Gabriella Dagher, Chen Hu, Rafal D Mazur, Elena M Kenney, Erin Brennan, Anna DuVal, Peter A Calabresi, Vadim Zipunnikov, Kathryn C Fitzgerald, Ellen M Mowry

JMIR Mhealth Uhealth 2025;13:e57599

Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence

Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence

(C) Low resolution: noisy image, blurry edge between embryo and uterine wall, and fourth ventricle visible. We used nn U-net to measure the EV and HV, which is a state-of-the-art publicly available segmentation method based on deep learning [23]. nn U-net configures itself automatically based on the available imaging data. The algorithm takes as input the 3 D ultrasound image and outputs the corresponding predicted segmentation.

Wietske A P Bastiaansen, Stefan Klein, Batoul Hojeij, Eleonora Rubini, Anton H J Koning, Wiro Niessen, Régine P M Steegers-Theunissen, Melek Rousian

J Med Internet Res 2025;27:e60887